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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rongali, Aruna Sri Qureshi, Anjum Nazir Upadhyay, Jyoti Lavanya, P. Matad, Nanditha S Jain, Sanjiv Kumar University of The Cumberlands Department of Information Technology United States Rajiv Gandhi College of Engineering Research and Technology Department of Electronics and Communication Chandrapur India Gd Rungta College of Science and Technology Department of Computer Science Chhattisgarh Bhilai India Institute of Aeronautical Engineering Hyderabad India New Horizon College Department of Mathematics Bangalore India Med-Caps University Department of Electrical Engineering Indore India

ISBN: (纸本)9798350370249

This paper explores the potential of machine-mastering models to automate network Anomaly Detection (NAD) through Time series analysis. We employ a two-level method wherein the primary degree entails function selection thru foremost component analysis (PCA), accompanied by gadget mastering (ML) model choice from more than a few supervised studying algorithms. The second stage evaluates the overall performance of the numerous selected ML models and optimizes theirhyperparameters when necessary. Our experiments demonstrate that ML-driven computerized network Anomaly Detection can provide accurate and well-timed detection of network anomalies with little supervision and parameter tuning attempts. The outcomes of our experiments display that Random Forests and Support Vector Machines (SVMs) carry out first-rate some of the model's grid searches, demonstrating aggressive accuracy and precision ratings from an anomaly detection perspective. We also intensely evaluate the consequences and provide insightful discussion on the possibilities and challenges surrounding using ML for automatic community Anomaly Detection. © 2024 IEEE.

关键词： Time series

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Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples

Discrete fully probabilistic design: towards a control pipel...

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Mediterranean Conference on Control and Automation (MED)

作者： Enrico Ferrentino Pasquale Chiacchio Giovanni Russo Department of Computer and Electrical Engineering and Applied Mathematics (DIEM) University of Salerno Fisciano SA Italy

We present the principled design of a control pipeline for the synthesis of policies from examples data. The pipeline, based on a discretized design, expounds the algorithm introduced in [1] to synthesize policies from examples for constrained, stochastic and nonlinear systems. The pipeline: (i) does not need the constraints to be fulfilled in the possibly noisy example data; (ii) enables control synthesis even when the data are collected from an example system that is different from the one under control. The design is benchmarked on an example that involves controlling an inverted pendulum with actuation constraints. The data that are used to synthesize the policy are collected from a pendulum that: (i) is different from the one under control; (ii) does not satisfy the actuation constraints.

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Improved PIR Schemes using Matching Vectors and Derivatives

arXiv

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arXiv 2024年

作者： Ghasemi, Fatemeh Kopparty, Swastik Sudan, Madhu Department of Mathematics University of Toronto Canada Department of Mathematics Department of Computer Science University of Toronto Canada School of Engineering and Applied Sciences Harvard University CambridgeMA United States

In this paper, we construct new t-server Private Information Retrieval (PIR) schemes with communication complexity subpolynomial in the previously best known, for all but finitely many t. Our results are based on combining derivatives (in the spirit of Woodruff-Yekhanin [WY05]) with the Matching Vector based PIRs of Yekhanin [Yek08] and Efremenko [Efr09]. Previously such a combination was achieved in an ingenious way by Dvir and Gopi [DG15], using polynomials and derivatives over certain exotic rings, en route to their fundamental result giving the first 2-server PIR with subpolynomial communication. Our improved PIRs are based on two ingredients: • We develop a new and direct approach to combine derivatives with Matching Vector based PIRs. This approach is much simpler than that of Dvir-Gopi: it works over the same field as the original PIRs, and only uses elementary properties of polynomials and derivatives. • A key subproblem that arises in the above approach is a higher-order polynomial interpolation problem. We show how "sparse S-decoding polynomials", a powerful tool from the original constructions of Matching Vector PIRs, can be used to solve this higher-order polynomial interpolation problem using surprisingly few higer-order evaluations. Using the known sparse S-decoding polynomials from [Efr09, IS08, CFL+13] in combination with our ideas leads to our improved PIRs. Notably, we get a 3-server PIR scheme with communication (formula presented), improving upon the previously best known communication of 2Õ(√log n) due to Efremenko [Efr09]. Copyright © 2024, The Authors. All rights reserved.

关键词： Vectors

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Nonlinear Young’s Modulus of New Red Sandstone:Experimental Studies

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Acta Mechanica Solida Sinica 2021年第6期34卷 989-999页

作者： Evgenii Riabokon Vladimir Poplygin Mikhail Turbakov Evgenii Kozhevnikov Dmitrii Kobiakov Mikhail Guzev Marian Wiercigroch Centre for Applied Dynamics Research School of EngineeringUniversity of AberdeenAberdeenUK Department of Oil and Gas Technologies Perm National Research Polytechnic UniversityPermRussia Institute of Applied Mathematics Far Eastern Branch of the Russian Academy of SciencesVladivostokRussia

Young’s modulus of New Red Sandstone was investigated experimentally to gain insight into its nonlinear nature.A large experimental programme was carried out by applying a controllable quasi-static and dynamic uniaxial loading to 286 dry sandstone samples of four different *** static and dynamic tests,similar to those aiming at determining the uniaxial compressive strength,were conducted using the state-of-the-art experimental facilities at the University of Aberdeen including a custom-built small experimental rig for inducing a dynamic uniaxial compressive load via a piezoelectric *** obtained results have confirmed a complex nature of Young’s modulus of ***,under a harmonic dynamic loading,it shows strongly nonlinear behaviour,which is hardening and softening with respect to frequency and amplitude of the dynamic loading,respectively.

关键词： Sandstone Young’s modulus Static and dynamic loading Experiments Stress and strain Nonlinearity

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A Deep Neural Network for Image Classification Using Mixed Analog and Digital Infrastructure 3rd

A Deep Neural Network for Image Classification Using Mixed A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Kala, R. Punitha, M. Poomani Alias Banupriya, P.G. Veerasamy, B. Bharathi, B. Alzubi, Jafar Ahmad Abed Associate professor Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Information Technology Sethu Institute of Technology Kariapatti Puloor India Department of Computer Science and Engineering Karpagam Institute of Technology Tamil Nadu Coimbatore641032 India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Information Technology Coimbatore Institute of Engineering and Technology Tamil Nadu Coimbatore641109 India School of Engineering Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789819986453

Recently, Deep Learning (DL) methods are a well-known solution for image and object detection in computer vision applications. The software-based implementation of DL algorithms requires huge resources and consumes more power. To overcome these challenges, a hardware implementation of Deep Neural Network (DNN) using the integrated analog–digital architecture was proposed in this article. The Very Large-Scale Integration (VLSI)-based DNN implementation utilizes Pulse-Width Modulation (PWM), and analog–digital circuits. The integration of analog and digital architecture integrates the benefits of both circuits, which reduces power consumption and enhances processing speed and classification accuracy. The fabrication of the VLSI chip includes the DNN neuron circuit, PWM circuit, digital adders-subtracters, and SRAM memory. The experimental result verifies that the presented DNN VLSI architecture reduces the power consumption of the neural system, and enhances the image classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Sustainable Agriculture-Based Food Security Analysis using Healthcare Data Modelling and Deep Learning Techniques

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Remote Sensing in Earth Systems Sciences 2024年第1期8卷 45-55页

作者： Reddy T, Rajaram Krishna Jangir, Pradeep Tamilarasi, G. Kumar, Ramatenki Sateesh Nitya, Erupaka Bakka, Vinod Kumar, Gaurav Department of English Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602 105 India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Virudhunagar Tamil Nadu Krishnankoil India Vasavi College of Engineering Hyderabad India Department of CSE(DS) CVR College of Engineering Telangana Hyderabad India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India School of Computer Applications Lovely Professional University Punjab India

The earth’s ecosystems are negatively impacted by the development of agriculture in both terrestrial and marine environments. Cropland losses, water scarcity, species infestations, land degradation, and climate change all work together to generate a 25% reduction in agricultural productivity. In order to achieve sustainable food production and security through the green revolution and environmentally benign methods, the world requires a paradigm shift in the development of agriculture. Therefore, agricultural practices must be supported by farmland’s capacity to produce enough food to meet human requirements forever, as well as by having long-term effects on the environment. This study offers a novel method for analysing healthcare in metropolitan areas based on food security using machine learning and remote sensing. In this instance, the data was gathered as health analysis data based on food security and processed to eliminate missing values. Then, a reinforcement recurrent term frequency Markov bi-directional Bayesian neural network was used to choose and classify this data. Two components: a text data training model and a server-side module for attribute estimation techniques. We tested with multiple food categories, each with hundreds of photographs, machine learning training, in an effort to achieve higher categorisation accuracy. In terms of F1-score, RMSE, recall, precision, and accuracy of predictions, an experimental investigation has been conducted. Proposed technique attained prediction accuracy 98%, F-1 score 95%, PRECISION 94%, Recall 97%, RMSE 52%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Bayesian neural network Food security Healthcare analysis Recurrent term frequency Remote sensing Urban region

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Rational Orthogonal Wavelet Pulse and Feature Extraction Method Based on Auditory Frequency Cepstral Coefficient for Underwater Target Localization 21

Rational Orthogonal Wavelet Pulse and Feature Extraction Met...

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21st International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2023

作者： Guo, Tiantian Lim, Eng Gee Lopez-Benitez, Miguel Fei, Ma Limin, Yu Department of Communications and Networking Suzhou China University of Liverpool Department of Electrical Engineering and Electronics Merseyside Liverpool United Kingdom ARIES Research Centre Antonio de Nebrija University Madrid28040 Spain Department Applied Mathematics Suzhou China

ISBN: (纸本)9798350303810

Underwater target localization has always been a challenging and important research topic in underwater acoustic sensing, especially when the underwater target is moving. This paper uses three pulse signals for system design: continuous wave, linear frequency modulated signal and rational orthogonal wavelet signal. We use a geometric underwater channel model to generate a database of underwater signals with specified geometric parameters of ocean environments to simulate the ocean. The received pulses signal are converted into feature maps as the classifier's input. In this paper, Short-time Fourier transform, Mel-frequency cepstral coefficient, Gammatone frequency cepstral coefficient and Perceptual linear prediction coefficient are applied to construct different feature maps. The classifier uses a lightweight CNN model. Experiments demonstrate the superiority of wavelet pulse signals in underwater target localization. The multipath effect will also contribute to underwater acoustic sensing. © 2023 IEEE.

关键词： CNN Gammatone frequency cepstral coefficient Mel frequency cepstral coefficient Rational orthogonal wavelet Underwater communication

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Guest Editorial to the Special Issue Public Transport Optimization: From Theory to Practice

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Operations research Forum 2024年第3期5卷 73页

作者： Cacchiani, Valentina Müller-Hannemann, Matthias Rojas-Marcos, Federico Perea Department of Electrical Electronic and Information Engineering “Guglielmo Marconi” University of Bologna Viale Risorgimento 2 Bologna 40136 Italy Institute for Computer Science Martin Luther University Halle-Wittenberg Von-Seckendorff-Platz 1 Halle D 06120 Germany Department of Applied Mathematics II and Institute of Mathematics (IMUS) University of Seville Avda. Reina Mercedes s/n Seville 41012 Spain

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Comment to Mobile Cellular Data and Wi-Fi Use Are Not Associated with Adverse Health Effects by Rabiei et al., Journal of Biomedical Physics and engineering

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Journal of Biomedical Physics and engineering 2023年第6期13卷 577-578页

作者： Arribas, Enrique Escobar, Isabel Ramirez-Vazquez, Raquel Department of Applied Physics Faculty of Computer Science Engineering University of Castilla-La Mancha University Campus Avda. de España s/n Albacete Spain MORFEO Research Group University of Castilla La Mancha Spain

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An Efficient AES 32-Bit Architecture Resistant to Fault Attacks

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computers, Materials & Continua 2022年第2期70卷 3667-3683页

作者： Hassen Mestiri Imen Barraj Abdullah Alsir Mohamed Mohsen Machhout Department of Computer Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Institute of Applied Sciences and Technology of Sousse University of SousseTunisia Electronics and Micro-Electronics Laboratory Faculty of Sciences of MonastirUniversity of MonastirTunisia METS Research Group Electrical Engineering DepartmentNational Engineers School of SfaxUniversity of SfaxTunisia Higher Institute of Computer Science and Multimedia of Gabes University of GabesTunisia

The Advanced Encryption Standard cryptographic algorithm,named AES,is implemented in cryptographic circuits to ensure high security level to any system which required confidentiality and secure information *** of the most effective physical attacks against the hardware implementation of AES is fault attacks which can extract secret *** now,a several AES fault detection schemes against fault injection attacks have been *** this paper,so as to ensure a high level of security against fault injection attacks,a new efficient fault detection scheme based on the AES architecture modification has been *** this reason,the AES 32-bit round is divided into two half rounds and input and pipeline registers are implemented between *** proposed scheme is independent of the procedure the AES is ***,it can be implemented to secure the pipeline and iterative *** evaluate the robustness of the proposed fault detection scheme against fault injection attacks,we conduct a transient and permanent fault attacks and then we determine the fault detection capability;it is about 99.88585%and 99.9069%for transient and permanent faults *** have modeled the AES fault detection scheme using VHDL hardware language and through hardware FPGA *** FPGA results demonstrate that our scheme can efficiently protect the AES hardware implementation against fault *** can be simply implemented with low *** addition,the FPGA implementation performances prove the low area overhead and the high efficiency and working frequency for the proposed AES detection scheme.

关键词： Security cryptographic circuits AES hardware implementation fault detection fault attacks

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